AI-100 : Preparing for the Azure AI Engineer certification

Written on November 1, 2020
Estimated reading time : 4 mins
Tags : | azure | certification | cloud |

In a hurry ? Please skip to the part you want with the index below.


“Knowledge is power. Knowledge shared is power multiplied.” - Robert Boyce

AI-100 is the Azure AI Engineer exam. I cleared the exam today ie Nov 1, 2020. This is my 3rd Azure certification. You can read about the others here.

I recently worked on Azure bot services for a project. It got me interested in Azure’s cognitive services. After my employer organized a related training session on AI-100, I got the drive to go for this certifrication.

"Azure AI Engineer"


Skills measured :

  • Analyze solution requirements (25-30%)
  • Design AI solutions (40-45%)
  • Implement and monitor AI solutions (25-30%)

As you see - this needs a lot of foundation knowledge (Azure + data engineering). I found 50% of the exam questions to involve the fundamentals of Azure. The rest was a mix of cogntitive services and other supporting tools. The exam is targeted for a data engineer who is comfortable with Azure. Since I cleared AZ-300 and AZ-301 recently, I too found the exam to be easy :smiley: . But it still involved going through a lot of course material !

There aren’t many AI-100 courses out there. But couple of websites which helped me prepare :

Cloud Academy

  • I recommend the AI-100 learning path.
  • Its well focused and short (5 hrs or so).
  • The course focuses on Azure’s cognitive services and some of the supporting tools.
  • Its expected for the audience to know the basics of Azure.

Udemy practice tests

  • This offering helped me a lot.
  • The 5 practice tests are really good and test your knowledge well. They also point out chinks in your Azure knowhow quite a bit.
  • The duration of each test was around 1 hour each though I completed each test within 30 min.

Scheduling the exam

You can schedule the exam through the Microsoft Certification Dashboard page.

Pre-exam process [Warning:Stress ahead!]

Like AZ-301, this was a bit of a stressful experience for me. I did the pre-exam prep below around 20 min before the exam :

  • I had to download a software which tested my machine for compatibility.
  • Send pics from 4 directions of my surroundings (closed room).
  • After a live 360 degrees scan from the Proctor, The exam was supposed to begin.
  • The pre exam profile questionaire wrapped up quickly.
  • However here it got a bit stressful :weary:
    • The software stopped working after it couldn’t stop some background app. It had me waiting for some response…but to no avail.
    • The supervisor mentioned some tech issue on the Mac version of the software and gave me a voucher to give the exam some other time in the future.
    • Scheduling the exam a 2nd time after 5 days didnt help. This time the app got stuck at ‘Loading’…and no amount of hitting the ‘retry’ button helped. Got another voucher again and rescheduled the exam after 3 days again.

The AI-100 exam

  • It was an exam of 3 hours with 30 questions and passing score of 70%.
  • Since I had completed the practice tests well in advance, I knew that time wasnt an issue for me.
  • The practice test experience helped. Found a lot of similar questions from the tests.
  • I submitted the exam in 30 min. Completed the questions in 25 min. Spent 5 min reviewing the questions about whom I wasn’t sure about.
  • I had to fill a survey on myself and nature of the exam after this (no impact on the exam results).
  • I immediately received the congratulatory message page for clearing the exam.

The exam report with certification id arrived in 10 minutes.

Final thoughts

  • I loved studying for A1-100. It really improves your basics on data engineering and reference architectures.
  • I think I might go for the data engineer path of certifications as my current work is pushing me towards it + I found it quite interesting when studying for AI-100.
  • Besides the main cognitive services, I focused on smaller topics which helped :
    • Azure reference architectures for AI (especially IoT and real time analytics use cases).
    • Azure on the Edge.
    • Machine learning concepts and tooling.
    • Azure Stream analytics.
    • HDInsight (Hadoop, Spark, Storm, Hive, Kafka etc).
    • Data Factory and Data Bricks.
  • Practice tests help a lot to identify chinks in your armour. Give as many as possible and multiple times if needed.

I wish you the best of luck if you plan on giving this exam :thumbsup:.
Feel free to share your experiences. Every bit of knowledge helps :blush:.

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